进化增强学习框架,用于WSNs中节能故障弹性和拓稳定性
S Lakshmi1, S Aswath2, A Swaminathan3
1Electronics and Communication Engineering, Thirumalai Engineering College, Kilambi, Kanchipuram, 631551, India. msklakshmi@gmail.com.
Scientific reports
|March 2, 2026
概括
智能框架EvoGenRL通过优化能源,延长寿命和通过集成学习和优化技术提高通信可靠性来增强无线传感器网络 (WSN).
科学领域:
- 计算机科学 计算机科学
- 网络工程 网络工程
- 人工智能的人工智能
背景情况:
- 无线传感器网络 (WSN) 对智慧城市和工业自动化至关重要.
- WSNs面临诸如能源耗尽,故障,延迟和寿命缩短等挑战,这些挑战因规模和动态而加剧.
- 当前的优化方法往往侧重于单个方面,忽视整体性能和弹性.
研究的目的:
- 介绍EvoGenRL,一个进化强化学习框架,用于稳健的WSN管理.
- 通过整合多种AI方法来解决现有的WSN优化技术的局限性.
- 在能源效率,通信质量和故障耐受性方面提高WSN性能.
主要方法:
- 综合强化学习 (RL) 适应性路由和容错政策.
- 差异演变 (DE) 用于优化超参数以提高趋同和政策效率.
- 生成对抗网络 (GANs) 用于创建多种故障场景以改善模型概括.
主要成果:
- 将能源消耗降低到2.2 J.
- 将网络寿命延长到1700个循环.
- 实现了99.7%的数据包交付比率,3.2 ms的延迟,以及350 kbps的吞吐量.
结论:
- 在WSN优化方面,EvoGenRL显著优于传统方法.
- 该框架成功地同时提高了能源效率,通信性能和故障弹性.
- EvoGenRL为智能,自适应和节能的WSN控制提供了一个可扩展的基础.
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